Anytime coalition structure generation with worst case guarantees
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Anytime coalition structure generation: an average case study
Proceedings of the third annual conference on Autonomous Agents
Customer coalitions in the electronic marketplace
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Generating Coalition Structures with Finite Bound from the Optimal Guarantees
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agent Technology For E-Commerce
Agent Technology For E-Commerce
An agent-based model for consumer-to-business electronic commerce
Expert Systems with Applications: An International Journal
Optimal Coalition Structure Generation In Partition Function Games
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Forming Buyer Coalitions with Bundles of Items
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Near-optimal anytime coalition structure generation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Electronic Commerce Research and Applications
Buyer coalitions with bundles of items by using genetic algorithm
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
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The study of the buyer coalition has been reviewed by researchers for decades. However, there are few schemes applying ant colony optimization (ACO) for forming buyer coalition. In this paper, the approach called the Ant Colony for Buyer Coalition (ACBC) algorithm is proposed to form the buyer coalition with bundles of items. The main endeavor of the algorithm is to partition the whole group of buyers into smaller groups in order to achieve a common goal. The artificial ants search to find best disjoint subgroups of all buyers based on the total utility functions. The experimental results are compared with the genetic algorithm (GAs) in the terms of the global optimal solution. It indicates that the algorithm can improve the total discount of the buyer coalition.